---
language:
- en
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
base_model: google/mt5-small
model-index:
- name: mt5-small-multi-news
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: multi_news
      type: multi_news
      config: default
      split: validation
      args: default
    metrics:
    - type: rouge
      value: 22.03
      name: Rouge1
    - type: rouge
      value: 6.95
      name: Rouge2
    - type: rouge
      value: 18.41
      name: Rougel
    - type: rouge
      value: 18.72
      name: Rougelsum
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# mt5-small-multi-news

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2170
- Rouge1: 22.03
- Rouge2: 6.95
- Rougel: 18.41
- Rougelsum: 18.72

## Intended uses & limitations

Text summarization is the inteded use of this model. With further training the model could achieve better results.

## Training and evaluation data

For the training data we used 10000 samples from the multi-news train dataset.
For the evaluation data we used 500 samples from the multi-news evaluation dataset.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 5.2732        | 1.0   | 1250 | 3.2170          | 22.03  | 6.95   | 18.41  | 18.72     |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3